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Getting the price right. A (hopefully) thought provoking article.

The leasing industry has been hit hard by the recession and has responded in several different ways. Everyone in the sector knows the stories about lessors cutting out broker business, seeking efficiencies, reducing headcount and so on.

Yet very few lessors have spent any time working out how to quote optimum prices in the market. By ‘optimum price’ I mean quoting the rental that is most likely to win the business whilst at the same time being as high as possible. Clearly, if you quote a price slightly higher than your competitor you will lose the deal. And if you quote well below your competitor you will win the deal but at a lower margin than you could have achieved. You should be trying to ensure that you select the price that is just below the level your competitor is likely to choose. Impossible? No, not at all.

And the prize is really worth going for. Let’s assume you could achieve an across-the-board 10bp margin increase if you could quote the optimum margin without losing any volume. That extra margin would go straight to your bottom line and would increase it by – how much? – 15%? More?

As my boss 30 years ago once told me; “Cost is irrelevant. Margin is the important thing, because that’s what pays for the shirt on your back and puts the food on your table.”

I often ask leasing company managers how they set their margins when doing their pricing. Once they get over the initial shock – asking someone about their pricing is a bit like asking them about their salary – they normally tell me they use one (or more) of six methods.

The first is “this is the way we have always done it. It’s worked for us in the past and we see no reason to change it”. Which, as a consultant, tends to take the wind out of my sails, until we start looking at the details and then discover the myriad ways in which the current method fails to deliver sensible prices. Very few lessors’ systems indentify the optimum margin; the one that will allow them to quote a price that will maximise their rentals and the volume of business they write.

The second approach to margin-setting relates to “The Plan”. The sales director commits the sales team to delivering a certain volume of business at a certain margin in a certain period. A target margin is then set – a higher number that includes a safety buffer to ensure the plan margin is actually delivered. “The plan says we must deliver this margin so we will do so”. Quoted margins start to reflect the target, even though the client doesn’t care about the target or the plan; he just wants the best price. The plan margin doesn’t help the salesperson calculate the optimum price to quote.

The third approach is to have a target margin for a particular sales channel or asset type. But here again this just tells the salesman the standard against which their performance will be measured, it doesn’t tell them how to set a price.

The fourth approach to use a return on equity calculation. Some of these are extremely complex but, here again, all they really do is establish a price that is acceptable to the lessor; they don’t produce the maximum price that would be acceptable to the lessee.

The fifth option is risk-based pricing. I’m a fan of this approach; charging all clients the same price doesn’t make sense to me, when some are so much less likely to default than others. Yet it is certainly still the case that one many lessors offer the same price across all their SME lessees, regardless of the financial strength of the client. There’s a lot of work to be done in this area but I do think it will lead to good results. However, an internally-calculated risk-based price is not much more likely to generate an optimum rental than any of the other methods mentioned above.

The final approach is that the salesperson uses their “commercial judgment” to decide the appropriate margin. There’s a lot to be said for a salesperson’s years of experience and I’m not going to decry this for one moment. But in a complex environment it doesn’t work. Let’s assume, for example, that we are talking about a salesperson in a fleet leasing company who is responsible for 25 major client accounts. There are around 7,000 model variants available in the UK market today, after taking into account all permutations of engine size, numbers of doors, fuel type, transmission type, etc.. Let’s say our fleet lessor is routinely asked to quote at 10 key mileage points (5,000 pa, 10,000 pa, 12,000 pa, etc) and for 10 key periods (18 months, 24 months, etc), with or without maintenance. Therefore the salesperson has to be able to produce 7000 x 10 x 10 x 2 x 25 = 35 million different optimum price quotes, instantly. And that’s before considering any manufacturer options. Clearly, commercial judgment is will only take you so far when you need to decide the optimum price in every situation.

I am a great admirer of people who own corner shops. They know how much each item of stock costs; they know their competitors, their customers and how much money they need to make by the end of the week. If a customer selects an item and asks for a discount, the shopkeeper knows the optimum price to sell it for. However, even the most experienced salesperson cannot hold in their head the optimum price to quote in 35 million situations. (Which is a shame, because if they could do so they would be just as able as the corner shopkeeper to produce an optimum price, time after time.)

In practice, lessors set margins at the client level, or at the sales channel level, or by using an ROE or risk-based calculation. However, these are just averages and as a result they end up losing deals for a few pence, or underbidding by many pounds.

Why don’t most lessors flex their margins, by customer, to reflect their recent success in winning business from that customer on that type of equipment? And why aren’t lessors’ systems configured to optimise price quotes at the point of sale, in real time, using all of the behavioural data held by the system in order to the to determine the maximum price that the client would be likely to accept?

The gains achievable from price optimisation are there to be taken; they are low hanging fruit for an industry that has suffered quite badly in the last couple of years and now needs to rebuild its balance sheets fast.